feat(ai): observability tracing + improved prompt templates
- Add AITrace type, AITraceStore (circular buffer, localStorage in DEV, window.__aiTraces for DevTools), provenanceToStatus() helper - Instrument OpenRouterAIService withFallback with latency tracking and trace recording across all three paths (no-key, success, error) - Wrap all MockAIService methods with traceMock for consistent in-memory tracing including method name, latency, and validation status - Improve all 6 prompt templates with ROLLE/AUFGABE/VERBOTE/BEISPIEL structure; marketSignalPrompt carries hard prohibition against claiming confirmed availability from unconfirmed signals Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Benjamin Sutter
@@ -17,6 +17,7 @@ import type {
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MarketSignalClassification,
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} from '../IAIService'
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import { mockProvenance } from '../IAIService'
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import { aiTraceStore } from '../tracing'
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import { mockParseNeed } from './needParser'
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import { buildComparisonSummary } from './compareBuilder'
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import { buildMockDecisionBrief } from './decisionBrief'
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@@ -24,6 +25,27 @@ import { buildMockDecisionBrief } from './decisionBrief'
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const SIMULATED_DELAY = { fast: 300, medium: 600, slow: 1800 }
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const delay = (ms: number) => new Promise(r => setTimeout(r, ms))
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// ── Tracing wrapper ───────────────────────────────────────────────────────────
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async function traceMock<T>(method: string, fn: () => Promise<AIResponse<T>>): Promise<AIResponse<T>> {
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const startMs = Date.now()
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const result = await fn()
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aiTraceStore.add({
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id: crypto.randomUUID(),
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method,
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provider: 'mock',
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model: 'mock',
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promptVersion: 'mock',
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latencyMs: Date.now() - startMs,
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fallbackUsed: false,
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validationPassed: true,
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responseValidationStatus: 'valid',
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source: 'mock',
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createdAt: new Date().toISOString(),
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})
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return result
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}
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// ── Follow-up question templates keyed by ParsedNeedCriteria field ────────────
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interface QuestionTemplate {
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@@ -99,141 +121,150 @@ function buildFollowUpQuestions(criteria: ParsedNeedCriteria): FollowUpQuestion[
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// ── Service ───────────────────────────────────────────────────────────────────
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export const MockAIService: IAIService = {
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async parseNeed(input: string): Promise<AIResponse<ReturnType<typeof mockParseNeed>>> {
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await delay(SIMULATED_DELAY.fast)
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return { data: mockParseNeed(input), provenance: mockProvenance() }
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},
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parseNeed: (input: string) =>
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traceMock('parseNeed', async () => {
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await delay(SIMULATED_DELAY.fast)
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return { data: mockParseNeed(input), provenance: mockProvenance() }
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}),
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async generateFollowUpQuestions(criteria: ParsedNeedCriteria): Promise<AIResponse<FollowUpQuestion[]>> {
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await delay(SIMULATED_DELAY.medium)
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return { data: buildFollowUpQuestions(criteria), provenance: mockProvenance() }
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},
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generateFollowUpQuestions: (criteria: ParsedNeedCriteria) =>
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traceMock('generateFollowUpQuestions', async () => {
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await delay(SIMULATED_DELAY.medium)
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return { data: buildFollowUpQuestions(criteria), provenance: mockProvenance() }
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}),
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async generateMatchExplanation(input: MatchExplanationInput): Promise<AIResponse<MatchExplanation>> {
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await delay(SIMULATED_DELAY.medium)
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const isStrong = input.matchScore >= 78
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const isMedium = input.matchScore >= 52
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const headline = isStrong
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? `Starkes Match — ${input.propertyTitle} erfüllt Ihre Kernkriterien hervorragend`
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: isMedium
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? `Gutes Match mit einzelnen Kompromissen für ${input.propertyTitle}`
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: `Schwaches Match — mehrere Kriterien nicht erfüllt bei ${input.propertyTitle}`
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const positiveText = input.positiveFactors.slice(0, 2).map(f => f.explanation).join('; ')
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const negativeText = input.negativeFactors.slice(0, 1).map(f => f.explanation).join('; ')
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const summary = `${input.propertyTitle} in ${input.propertyCity} erreicht ${input.matchScore}/100 Punkte.${positiveText ? ` Hauptstärken: ${positiveText}.` : ''}${negativeText ? ` Einschränkung: ${negativeText}.` : ''}`
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return {
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data: {
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headline,
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summary,
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keyReasons: [
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...input.positiveFactors.slice(0, 2).map(f => `+ ${f.explanation}`),
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...input.negativeFactors.slice(0, 1).map(f => `− ${f.explanation}`),
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],
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},
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provenance: mockProvenance(),
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}
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},
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generateMatchExplanation: (input: MatchExplanationInput) =>
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traceMock('generateMatchExplanation', async () => {
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await delay(SIMULATED_DELAY.medium)
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const isStrong = input.matchScore >= 78
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const isMedium = input.matchScore >= 52
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const headline = isStrong
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? `Starkes Match — ${input.propertyTitle} erfüllt Ihre Kernkriterien hervorragend`
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: isMedium
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? `Gutes Match mit einzelnen Kompromissen für ${input.propertyTitle}`
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: `Schwaches Match — mehrere Kriterien nicht erfüllt bei ${input.propertyTitle}`
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const positiveText = input.positiveFactors.slice(0, 2).map(f => f.explanation).join('; ')
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const negativeText = input.negativeFactors.slice(0, 1).map(f => f.explanation).join('; ')
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const summary = `${input.propertyTitle} in ${input.propertyCity} erreicht ${input.matchScore}/100 Punkte.${positiveText ? ` Hauptstärken: ${positiveText}.` : ''}${negativeText ? ` Einschränkung: ${negativeText}.` : ''}`
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return {
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data: {
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headline,
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summary,
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keyReasons: [
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...input.positiveFactors.slice(0, 2).map(f => `+ ${f.explanation}`),
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...input.negativeFactors.slice(0, 1).map(f => `− ${f.explanation}`),
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],
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},
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provenance: mockProvenance(),
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}
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}),
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async summarizeTradeOffs(tradeoffs: TradeOffInput[]): Promise<AIResponse<TradeOffSummary>> {
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await delay(SIMULATED_DELAY.fast)
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const critical = tradeoffs.filter(t => t.severity === 'HIGH')
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const overallRisk: TradeOffSummary['overallRisk'] =
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critical.length >= 2 ? 'HIGH' : critical.length === 1 ? 'MEDIUM' : 'LOW'
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const riskLabel = overallRisk === 'HIGH' ? 'Hoch' : overallRisk === 'MEDIUM' ? 'Mittel' : 'Gering'
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return {
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data: {
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headline: tradeoffs.length === 0
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? 'Keine wesentlichen Trade-offs identifiziert'
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: `${tradeoffs.length} Trade-off${tradeoffs.length > 1 ? 's' : ''} — Gesamtrisiko: ${riskLabel}`,
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items: tradeoffs.map(t => ({ concern: t.concern, severity: t.severity, mitigation: t.mitigation })),
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overallRisk,
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},
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provenance: mockProvenance(),
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}
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},
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summarizeTradeOffs: (tradeoffs: TradeOffInput[]) =>
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traceMock('summarizeTradeOffs', async () => {
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await delay(SIMULATED_DELAY.fast)
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const critical = tradeoffs.filter(t => t.severity === 'HIGH')
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const overallRisk: TradeOffSummary['overallRisk'] =
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critical.length >= 2 ? 'HIGH' : critical.length === 1 ? 'MEDIUM' : 'LOW'
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const riskLabel = overallRisk === 'HIGH' ? 'Hoch' : overallRisk === 'MEDIUM' ? 'Mittel' : 'Gering'
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return {
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data: {
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headline: tradeoffs.length === 0
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? 'Keine wesentlichen Trade-offs identifiziert'
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: `${tradeoffs.length} Trade-off${tradeoffs.length > 1 ? 's' : ''} — Gesamtrisiko: ${riskLabel}`,
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items: tradeoffs.map(t => ({ concern: t.concern, severity: t.severity, mitigation: t.mitigation })),
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overallRisk,
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},
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provenance: mockProvenance(),
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}
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}),
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async summarizeComparison(items: UnifiedMatchResult[]): Promise<AIResponse<ComparisonSummary>> {
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await delay(SIMULATED_DELAY.medium)
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return { data: buildComparisonSummary(items), provenance: mockProvenance() }
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},
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summarizeComparison: (items: UnifiedMatchResult[]) =>
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traceMock('summarizeComparison', async () => {
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await delay(SIMULATED_DELAY.medium)
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return { data: buildComparisonSummary(items), provenance: mockProvenance() }
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}),
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async generateDecisionBrief(shortlistId: string): Promise<AIResponse<DecisionBrief>> {
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await delay(SIMULATED_DELAY.slow)
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return { data: buildMockDecisionBrief(shortlistId), provenance: mockProvenance() }
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},
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generateDecisionBrief: (shortlistId: string) =>
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traceMock('generateDecisionBrief', async () => {
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await delay(SIMULATED_DELAY.slow)
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return { data: buildMockDecisionBrief(shortlistId), provenance: mockProvenance() }
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}),
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async generateDataQualitySummary(_propertyId: string, quality: DataQualityInput): Promise<AIResponse<DataQualitySummary>> {
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await delay(SIMULATED_DELAY.fast)
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const level =
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quality.score >= 0.85 ? 'excellent'
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: quality.score >= 0.70 ? 'good'
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: quality.score >= 0.55 ? 'fair'
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: quality.score >= 0.40 ? 'poor'
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: 'critical'
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const assessments: Record<string, string> = {
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excellent: 'Exzellente Datenqualität — alle Kernfelder vollständig und aktuell.',
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good: 'Gute Datenqualität — kleinere Lücken beeinflussen die Matchgenauigkeit nicht wesentlich.',
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fair: 'Ausreichende Datenqualität — fehlende Felder können die Matchgenauigkeit beeinträchtigen.',
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poor: 'Geringe Datenqualität — wichtige Felder fehlen, Match-Score mit Vorsicht interpretieren.',
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critical: 'Kritische Datenqualität — fundamentale Felder fehlen, Match-Ergebnis stark eingeschränkt.',
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}
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const hasCritical = quality.missingCriticalFields.length > 0
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return {
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data: {
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overallAssessment: assessments[level],
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missingCriticalFields: quality.missingCriticalFields,
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recommendation: hasCritical
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? `Fehlende Pflichtfelder ergänzen: ${quality.missingCriticalFields.join(', ')}`
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: quality.score < 0.70
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? 'Daten aktualisieren und optionale Felder ergänzen für bessere Matchgenauigkeit.'
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: 'Keine sofortigen Massnahmen erforderlich.',
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confidence: quality.score,
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},
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provenance: mockProvenance(),
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}
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},
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generateDataQualitySummary: (_propertyId: string, quality: DataQualityInput) =>
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traceMock('generateDataQualitySummary', async () => {
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await delay(SIMULATED_DELAY.fast)
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const level =
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quality.score >= 0.85 ? 'excellent'
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: quality.score >= 0.70 ? 'good'
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: quality.score >= 0.55 ? 'fair'
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: quality.score >= 0.40 ? 'poor'
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: 'critical'
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const assessments: Record<string, string> = {
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excellent: 'Exzellente Datenqualität — alle Kernfelder vollständig und aktuell.',
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good: 'Gute Datenqualität — kleinere Lücken beeinflussen die Matchgenauigkeit nicht wesentlich.',
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fair: 'Ausreichende Datenqualität — fehlende Felder können die Matchgenauigkeit beeinträchtigen.',
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poor: 'Geringe Datenqualität — wichtige Felder fehlen, Match-Score mit Vorsicht interpretieren.',
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critical: 'Kritische Datenqualität — fundamentale Felder fehlen, Match-Ergebnis stark eingeschränkt.',
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}
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const hasCritical = quality.missingCriticalFields.length > 0
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return {
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data: {
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overallAssessment: assessments[level],
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missingCriticalFields: quality.missingCriticalFields,
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recommendation: hasCritical
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? `Fehlende Pflichtfelder ergänzen: ${quality.missingCriticalFields.join(', ')}`
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: quality.score < 0.70
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? 'Daten aktualisieren und optionale Felder ergänzen für bessere Matchgenauigkeit.'
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: 'Keine sofortigen Massnahmen erforderlich.',
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confidence: quality.score,
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},
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provenance: mockProvenance(),
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}
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}),
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async classifyMarketSignal(signalText: string): Promise<AIResponse<MarketSignalClassification>> {
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await delay(SIMULATED_DELAY.medium)
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const t = signalText.toLowerCase()
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let signalType: MarketSignalClassification['signalType'] = 'UNKNOWN'
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if (t.includes('neubau') || t.includes('baubewilligung') || t.includes('umbau')) signalType = 'CONSTRUCTION'
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else if (t.includes('expansion') || t.includes('wachstum') || t.includes('sucht fläche')) signalType = 'EXPANSION'
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else if (t.includes('verlegt') || t.includes('umzug') || t.includes('relocation')) signalType = 'RELOCATION'
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else if (t.includes('stellenabbau') || t.includes('restruktur') || t.includes('fusion')) signalType = 'RESTRUCTURING'
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else if (t.includes('frei') || t.includes('kündigung') || t.includes('schliessung') || t.includes('leerstand')) signalType = 'VACANCY'
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const areaMatch = signalText.match(/(\d{2,5})\s*m²/)
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const monthsMatch = signalText.match(/(\d{1,2})\s*Monate?n?/)
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return {
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data: {
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signalType,
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probability: 0.65,
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timeHorizonMonths: monthsMatch ? parseInt(monthsMatch[1]) : null,
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areaSqmEstimate: areaMatch ? parseInt(areaMatch[1]) : null,
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credibility: 'MEDIUM',
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reasoning: `Keyword-basierte Klassifikation (Mock). Signaltyp: ${signalType}.`,
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},
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provenance: mockProvenance(),
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}
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},
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classifyMarketSignal: (signalText: string) =>
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traceMock('classifyMarketSignal', async () => {
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await delay(SIMULATED_DELAY.medium)
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const t = signalText.toLowerCase()
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let signalType: MarketSignalClassification['signalType'] = 'UNKNOWN'
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if (t.includes('neubau') || t.includes('baubewilligung') || t.includes('umbau')) signalType = 'CONSTRUCTION'
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else if (t.includes('expansion') || t.includes('wachstum') || t.includes('sucht fläche')) signalType = 'EXPANSION'
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else if (t.includes('verlegt') || t.includes('umzug') || t.includes('relocation')) signalType = 'RELOCATION'
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else if (t.includes('stellenabbau') || t.includes('restruktur') || t.includes('fusion')) signalType = 'RESTRUCTURING'
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else if (t.includes('frei') || t.includes('kündigung') || t.includes('schliessung') || t.includes('leerstand')) signalType = 'VACANCY'
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const areaMatch = signalText.match(/(\d{2,5})\s*m²/)
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const monthsMatch = signalText.match(/(\d{1,2})\s*Monate?n?/)
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return {
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data: {
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signalType,
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probability: 0.65,
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timeHorizonMonths: monthsMatch ? parseInt(monthsMatch[1]) : null,
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areaSqmEstimate: areaMatch ? parseInt(areaMatch[1]) : null,
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credibility: 'MEDIUM',
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reasoning: `Keyword-basierte Klassifikation (Mock). Signaltyp: ${signalType}.`,
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},
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provenance: mockProvenance(),
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}
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}),
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async generateOfferEmail(payload: OfferEmailPayload): Promise<AIResponse<{ subject: string; body: string }>> {
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await delay(SIMULATED_DELAY.medium * 2)
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return {
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data: {
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subject: `Passende Gewerbeflächen zu Ihrer Anfrage: ${payload.needTitle}`,
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body:
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`Sehr geehrte Damen und Herren,\n\nvielen Dank für Ihr Interesse. Gerne unterbreiten wir Ihnen folgende passende Gewerbeobjekte aus unserem Portfolio:\n\n` +
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payload.properties.map((p, i) => `• ${p} (Match-Score: ${payload.matchScores[i]}%)`).join('\n') +
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`\n\nGerne arrangieren wir Besichtigungstermine für die genannten Objekte und stehen für alle weiteren Fragen zur Verfügung.\n\nFreundliche Grüsse\nWincasa AG`,
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},
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provenance: mockProvenance(),
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}
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},
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generateOfferEmail: (payload: OfferEmailPayload) =>
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traceMock('generateOfferEmail', async () => {
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await delay(SIMULATED_DELAY.medium * 2)
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return {
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data: {
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subject: `Passende Gewerbeflächen zu Ihrer Anfrage: ${payload.needTitle}`,
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body:
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`Sehr geehrte Damen und Herren,\n\nvielen Dank für Ihr Interesse. Gerne unterbreiten wir Ihnen folgende passende Gewerbeobjekte aus unserem Portfolio:\n\n` +
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payload.properties.map((p, i) => `• ${p} (Match-Score: ${payload.matchScores[i]}%)`).join('\n') +
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`\n\nGerne arrangieren wir Besichtigungstermine für die genannten Objekte und stehen für alle weiteren Fragen zur Verfügung.\n\nFreundliche Grüsse\nWincasa AG`,
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},
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provenance: mockProvenance(),
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}
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}),
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// Legacy methods
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async extractCriteria(_input: string): Promise<AIResponse<CriteriaExtractionResult>> {
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return {
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extractCriteria: (_input: string) =>
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traceMock('extractCriteria', async () => ({
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data: {
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extractedCriteria: {
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companyName: 'Unbekannt (bitte bestätigen)',
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@@ -250,15 +281,15 @@ export const MockAIService: IAIService = {
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],
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},
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provenance: mockProvenance(),
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}
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},
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})),
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async generateFollowUp(partialNeed: Partial<CreateNeedInput>): Promise<AIResponse<string[]>> {
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const questions: string[] = []
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if (!partialNeed.assetType) questions.push('Welchen Nutzungstyp suchen Sie?')
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if (!partialNeed.preferredLocations?.length) questions.push('In welchen Regionen suchen Sie?')
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if (!partialNeed.timing) questions.push('Was ist Ihr gewünschter Einzugstermin?')
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if (!partialNeed.budgetRange) questions.push('Was ist Ihr maximales monatliches Budget?')
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return { data: questions, provenance: mockProvenance() }
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},
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generateFollowUp: (partialNeed: Partial<CreateNeedInput>) =>
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traceMock('generateFollowUp', async () => {
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const questions: string[] = []
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if (!partialNeed.assetType) questions.push('Welchen Nutzungstyp suchen Sie?')
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if (!partialNeed.preferredLocations?.length) questions.push('In welchen Regionen suchen Sie?')
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if (!partialNeed.timing) questions.push('Was ist Ihr gewünschter Einzugstermin?')
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if (!partialNeed.budgetRange) questions.push('Was ist Ihr maximales monatliches Budget?')
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return { data: questions, provenance: mockProvenance() }
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}),
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}
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